The telecommunications industry has been expanding at an unprecedented growth rate throughout the World. In particular, the wireless sector, including cell phones, PCS, wireless local area networks and Bluetooth such as IS95, GSM, 3G, IEEE 802.11a/b/g, and 802.16 has grown far beyond expectations and at a much higher rate than the fixed telecommunications (wired) counterpart. The ability to access data and communicate anywhere at anytime has enormous potential and commercial value.
The content of the wireless sector is also changing, with more and more data being transmitted, including Internet connectivity and live feeds. The usage involving personal digital assistants (PDA's) and even smart appliances have created new markets utilizing wireless data communications. Despite advancements in wireless transmission and reception, there is a growing problem of extracting more information signals within a limited bandwidth.
Emerging multiple-access receiver processing procedures allow for multiple users to access the same communications medium to transmit or receive information. Multiple access communication systems allow the transmission of multiple digital data streams between multiple transmitting and receiving devices. However, since many users transmit energy on the same communications channel, a number of inherent difficulties arise, particularly when receivers attempt to detect the information associated with a particular user when there is heavy signal interference created by other users of the system at the same time. Typically the signal of interest cannot be received or the quality of reception is significantly degraded.
For example, a base station that processes a number of cellular devices has to receive and transmit data within a certain frequency range. The ability to extract the correct data from a given user is a difficult task when the effects of interference and multipaths are considered. The problem is further complicated when the number of users exceeds the number of dimensions (e.g. time slots, frequency slots, polarizations, etc), resulting in an overloaded condition.
In addition to the problems associated with multiple users in a given bandwidth, an additional problem is the inability to process the data in the receivers in real time. Advanced receiver techniques cover several areas, namely interference suppression (also called Multi-User Detection or MUD), multipath combining and space-time processing, equalization, and channel estimation. These various techniques can be mixed and matched depending upon the circumstances.
One way of alleviating some of the multiple access problems is to separate the interfering transmissions at the receiver using signal processing techniques. However, state of the art receivers are not capable of detecting and decoding the information associated with each user under conditions of heavy interference. Another solution to the co-channel interference problem is to decrease the number of users per channel. This, of course, is not an attractive option for telecommunication companies, since obtaining the maximum number of users or managing peak volume transmission periods are important business objectives.
It should be understood that the discussion herein illustrates wireless cellular communications the multiple access topologies are equally applicable to wired cable systems and local area networks, read/write operations of a disc drive, satellite communications and any application that benefits from extracting digital information from among many multiple interfering signals.
Several techniques are used to improve results in co-channel multiple access communications systems. Frequency-Division Multiple Access (FDMA) assigns a different frequency to each user and parses an allocated band for a communication system wherein a single user's signal transmission power is concentrated into a single narrower radio frequency band. Interference from adjacent channels is limited by the use of band pass filters, however for each channel being assigned a different frequency system the total capacity is limited by the available frequency slots and by physical limitations imposed by frequency reuse. In a cellular telephone configuration this poses problems because all proximate cells must operate on different frequencies. However, frequency bands may be re-used, provided that the same frequency cells are positioned at a certain distance apart. A further drawback with FDMA schemes is that users will pay full-time for their assigned frequency regardless of their actual use of the system.
Code Division Multiple Access (CDMA) is another multiplexing technique wherein for each communication channel the signals are encoded using a sequence known to the transmitter and the receiver for that specific channel. In CDMA, all users use the same frequency at the same time. However, before transmission, the signal from each user is multiplied by a distinct signature waveform. The signature waveform is a signal that has a larger bandwidth than the information-bearing signal from the user. However, in a CDMA system, the total level of co-channel interference limits the number of active users at any instant of time.
In Time Division Multiple Access (TDMA) technology, multiple channels of data are temporally interleaved, i.e. each signal is assigned to a different time interval and the signals are transmitted individually, according to their assigned time slot. The TDMA channel consists of a time slot or frame in a periodic train of time intervals over the same frequency, with a given signal's energy confined to one of these time slots. However, in a TDMA system, all transmitters and receivers must have access to a common clock, as time-synchronization among the users is required. Adjacent channel interference is limited by the use of a time gate or other synchronization element that only passes signal energy received at the proper time. The system capacity is limited by the available time slots (within a given frequency band) as well as by physical limitations imposed by frequency reuse, as each channel is assigned a different time slot within a particular frequency band.
One of the goals of FDMA and TDMA systems is to try and prevent two potentially interfering signals from occupying the same frequency at the same time. In contrast, Code Division Multiple Access (CDMA) techniques allow signals to overlap in both time and frequency. CDMA signals share the same frequency spectrum at the same time, hence, the CDMA signals appear to overlap one another. The scrambled signal format of CDMA virtually eliminates cross talk between interfering transmitters.
In a CDMA system, each signal is transmitted using spread spectrum techniques. The transmitted informational data stream is impressed upon a much higher rate data stream termed a signature sequence. The bit stream of the signature sequence data is typically binary, and can be generated using a pseudo-noise (PN) process that appears random, but can be replicated by an authorized receiver. The informational data stream and the high bit rate signature sequence stream are combined by multiplying the two bit streams together, assuming the binary values of the two bit streams are represented by +1 or −1. This combination of the higher bit rate signal with the lower bit rate data stream is called spreading the informational data stream signal. Each informational data stream or channel is allocated a unique signature sequence.
In operation, a stream of spread information signature signals are modulated by weights corresponding to the information that is to be transmitted. Some modulation examples include binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK). If several transmitters modulate their data onto the signature waveform and modulate again with the carrier tone, a radio frequency (RF) signal comprised of a continuous stream of information modulated signature pulses will be present at the receiver, one corresponding to each transmitter. The plurality of transmitted signals and are jointly received as a composite signal at the receiver. Each of the spread signals overlaps all of the other spread signals in time and frequency. Moreover, environmental noise as well as receiver electronic noise is also present in the measured received signal. The state of the art receiver correlates the composite noisy signal with one of the unique signature sequences, and the corresponding information signal is isolated and despread while the other signals appear as only small additions to the noise floor.
A signature sequence is often used to represent one bit of information. Receiving the transmitted sequence or its complement indicates whether the information bit is a+1 or −1, sometimes denoted “0” or “1”. The signature sequence usually comprises N pulses, and each pulse is called a “chip”. The entire N-chip sequence, or its complement, depending on the information bit to be conveyed, is referred to as a transmitted symbol.
The receiver correlates the received signal with the complex conjugate of the known signature sequence to produce a correlation value. When a ‘large’ positive correlation results, a “0” is detected, and when a ‘large’ negative correlation results, a “1” is detected.
It should be understood that the information bits could also be coded bits, where the code is a block or convolutional code. Also, the signature sequence can be much longer than a single transmitted symbol, in which case a subsequence of the signature sequence is used to spread the information bit.
The prior systems do not properly account for the real world mobile communication signals that suffer from signal degradation such as interference and multipath problems. The systems of the state of the art generally tended to make assumptions that all other interferers and multipaths were additive white Gaussian noise. However, this assumption is not accurate for co-channel interference and multipaths.
Multipath dispersion occurs when a signal proceeds to the receiver along not one but many paths so that the receiver encounters echoes having different and randomly varying delays and amplitudes. The receiver receives a composite signal of multiple versions of the transmitted symbol that have propagated along different paths, called rays, having different relative time. Each distinguishable ray has a certain relative time of arrival, a certain amplitude and phase, and as a result, the correlator outputs several smaller spikes. RAKE receivers are well known and attempt to ‘rake’ together all the contributions to detect the transmitted symbol and recover the information bit.
Conventional RAKE receivers provide satisfactory performance for operation in the presence of multipath under ideal conditions however the signature sequence must be uncorrelated with time shifted versions of itself as well as various shifted versions of the signature sequences of the other CDMA signals. Co-channel interference refers to signals received from other users either directly or reflected. If one received signal corresponding to the signature sequence of interest has a non-negligible cross correlation with the received signal originating from another transmitter (a co-channel interferer), then the value measured at the receiver, e.g. the correlation value for the signal of interest, is corrupted. In other words, the correlation computed at the receiver that would be used to decode a particular signal of interest is overwhelmed by an interfering signal; this is referred to as the near-far problem. The interference caused by an echo of one transmitted symbol overlapping with the next transmitted symbol might also be non-negligible. If this is the case, the transmitted symbols interfere with past and future transmitted symbols. This is commonly referred to as intersymbol interference (ISI). In actuality, performance is degraded both by co-channel interference and ISI.
There has been much research to address signal interference with known multipath time dispersion. This is termed joint demodulation with no multipath and is further described in S. Verdu, “Minimum Probability of Error For Asynchronous Gaussian Multiple-Access Channels,” IEEE Trans. Info. Theory, Vol. IT-32, pp. 85-96, R. Lupas and S. Verdu, “Linear multiuser detectors for synchronous code-division multiple-access channels,” IEEE Trans. Inform. Theory, Vol. 35, pp. 123-136, January 1989; and R. Lupas and S. Verdu, “Near-far resistance of multiuser detectors in asynchronous channels,” IEEE Trans. Commun., Vol. 38, pp. 496-508, April 1990.
There are a host of approaches for jointly demodulating any set of interfering digitally modulated signals, including multiple digitally modulated signals. Maximum Likelihood Sequence Estimation determines the most likely set of transmitted information bits for a plurality of digital signals without multipath time dispersion. The maximum likelihood joint demodulator is capable, in theory, of accommodating the largest number of interfering signals, but has a prohibitive computational complexity that makes it unrealizable in practice. The decorrelation receiver is another, less computationally complex receiver processing approach that zeroes out or decorrelates the different signals so that they no longer interfere with one another. The decorrelator as well as virtually every other lower complexity joint demodulator, is not capable of operation when the number of signals is over a set threshold which falls significantly short of the theoretical maximum.
In a real world multi-user system, there are a number of independent users simultaneously transmitting signals. These transmissions have the real-time problems of multi-path and co-channel interference, fading, and dispersion that affect the received signals. As described in the prior art, multiple user systems communicate on the same frequency and at the same time by utilizing parameter and channel estimates that are processed by a multi-user detector. The output of the optimal multi-user detector operating within the multiuser capacity limits of the channel is an accurate estimation as to the individual bits for an individual user.
Moreover, in an article by Paul D. Alexander, Mark C. Reed, John A. Asenstorfer and Christian B. Schlagel in IEEE Transactions on Communications, vol. 47, number 7, July 1999, entitled “Iterative Multi-User Interference Reduction: Turbo CDMA,” a system is described in which multiple users can transmit coded information on the same frequency at the same time, with the multi-user detection system separating the scrambled result into interference-free voice or data streams.
Low complexity multiuser detector have been contemplated that use linear multiuser detectors to achieve optimal near-far resistance. (Near-Far Resistance of Multiuser Detectors for Coherent Multiuser Communications, R. Lupas, S. Verdu, IEEE Trans. Commun. Vol. 38, no. 4, pp 495-508, April 1990). While providing certain advantages, the performance has not been demonstrably improved. Varanasi and Aazhang proposed a multistage technique as described in the article Near-Optimum Detection in Synchronous Code-Division Multiple Access Systems, IEEE Trans. Commun., Vol. 39, No. 5, May 1991.
Decorrelating decision feedback detectors (DDFD) have been described by A. Duel-Hallen in Decorrelating Decision-Feedback Multiuser Detector for Synchronous Code-division Multiple Access Channel, IEEE Trans. Commun., Vol. 41, pp 285-290, February 1993. Wei and Schlegel proposed soft-decision feedback to suppress error propagation of the DDFD in Synchronous DS-SSMA with Improved Decorrelating Decision-Feedback Multiuser Detection, IEEE Trans. Veh. Technol., Vol. 43, pp 767-772, August 1994. Tree-type maximum-likelihood sequence detectors were also proposed for multiuser systems as were breadth-first algorithms and sequential detection including using the M-algorithm tree-search scheme with a matched filter (MF). The prior references also reveal schemes that include some form of decorrelating noise whitening filter (WF).
However, one of the primary disadvantages of the prior references implementations is the inability to accommodate overloaded conditions. Decision feedback techniques are limited in that they are incapable of working in supersaturated environments. Although the MMSE-based decision feedback detector can work in a supersaturated environment, it has been demonstrated to be too aggressive with hypothesis testing to produce accurate results.
Multi-user detection (MUD) refers to the detection of data in non-orthogonal multiplexes. MUD processing increases the number of bits available per chip or signaling dimension for systems having interference limited systems. A MUD receiver jointly demodulates co-channel interfering digital signals. Multiuser detection systems take full advantage of all information available at the receiver, by making use of any “knowledge” that the receiver has about the interfering signals. Because the number of users that can be packed into a MUD-based multiple access (MA) system is a function of the number of independent dimensions over which the set of signals is spread (the dimension of the span of the set of signals), the total number of users in the system can be increased if more dimensions are used for transmitting the signals and the same dimensions are accessible at the receiver.
In addition to expanding the number of dimensions, favorably “spreading” the received signals out over those dimensions can also allow for increases in the number of users a MUD-based system can accommodate. For example, typical signaling sets for multiuser communications do not include as a free parameter the reference amplitude of each user. In the IS95 code division protocol, amplitude is controlled completely for purposes of power control to meet a signal-to-noise specification (all users ideally being received with the same signal-to-noise ratio (SNR)). Therefore the advantages offered to the MUD are not exploited and the aggregate throughput of a multiple access system is limited if amplitude is not exploited.
There are various multiuser detectors in the art, including optimal or maximum likelihood MUD, maximum likelihood sequence estimator for multiple interfering users, successive interference cancellation, TurboMUD or iterative MUD, and various linear algebra based multi-user detectors such as all of those detailed in the well-known text “Multiuser Detection” by Sergio Verdu. In the state of the art, algebraic means are used to compute linear operators for the entire set of users (communications channels) simultaneously. This is done by utilizing prior information, or knowledge of the likely value of each user's bit of information, each at a particular instant in time. This multiuser detection processing is described in the text S. Verdu, Multiuser Detection, Cambridge Press, 1998. However, this suffers from a significant disadvantage in that it requires knowledge of all parameters to perform the processing.
Optimal MUD based on the maximum likelihood sequence estimator operates by comparing the received signal with the entire number of possibilities that could have resulted, one for each bit or symbol epoch. The number of possible measured levels for the received signal is exponentially related to the number of users and the duration of the ISI. Hence, the optimal processing is a computationally complex and it is not possible to accomplish in a real-time environment. Thus for those multi-user detectors that examine the entire space, real-time operation is often elusive.
In general, optimal MUD units function by examining a number of possibilities for each bit. However, for multi-user detectors that examine a larger capacity of signal, the computations are complex and time-consuming, thus making real-time operation impossible. Numerous attempts at reliable pruning of the optimal MUD decision process or the use of linear approximation to the replace the optimal MUD have still not produced a workable solution for the real world environment.
There are several suboptimal multiuser detectors that are less computationally complex and known in the art. One example of suboptimal detectors, called linear detectors, includes decorrelators, minimum mean square error or MMSE detectors, and zero-forcing block linear equalizers. The conventional Minimum Mean Squared Error (MMSE) Multiuser detector utilizing prior information is described by Wang and Poor in “Iterative (Turbo) Soft Interference Cancellation and Decoding for Coded CDMA”, in the Transactions on Communications, July 1999. See also Alexander, Reed, Asenstorfer, and Schlegel, “Iterative Multiuser Interference Reduction: Turbo CDMA,” IEEE Trans on Comm, July 1999; and Poor, “Turbo Multiuser Detection: An Overview” ISSSTA 2000. But, linear algebra based MUD (non-iterative) and successive interference cancellation fails for cases of overloaded multiple access systems.
One example of overloading is where the number of simultaneous users is doubled or tripled relative to existing state of the art. Even for underloaded multiple access systems, the performance of non-iterative MUD and successive interference cancellation degrades significantly as the number of users increases, while the computation complexity of the optimal MUD increases significantly as the number of users increases. The computing problems are so extreme that even the most expensive hardware unbound by size and weight can often to keep us with this overwhelming complex processing requirement of optimal MUD. Moreover, an unreasonable delay would be required to decode each bit or symbol rendering such a system useless in practice.
Reduced complexity approaches based on tree-pruning help to some extent to eliminate the improper bit combination from consideration where, ideally, such a procedure should prune out many ‘bad’ paths in the decision tree but maintain the proper path. Thus, the entire tree does not need to be traversed to make the final decision.
The M-algorithm is a pruning process that limits the number of hypotheses extended to each stage to a fixed tree width and prunes based on ranking metrics for all hypotheses and retaining only the M most likely hypotheses. The T-algorithm prunes hypotheses by comparing the metrics representing all active hypotheses to a threshold based on the metric corresponding to the most-likely candidate. Performance of M-algorithm based MUD degrades as the parameter M is decreased, but M governs the number of computations required. Similar effects are seen for other tree-pruning based MUD (T-algorithm, etc). To combat improper pruning, basic tree-pruning must ensure that M is “large enough”, and therefore still encounters increased complexity for acceptable performance levels when the number of interfering signals and/or ISI lengths are moderate to large.
As an illustration of the M-algorithm as a tree-pruning algorithm, consider a tree made up of nodes and branches. Each branch has a weight or metric, and a complete path is sequences of nodes connected by branches between the root of the tree and its branches. When applied as a short cut to the optimal MUD, each branch weight is a function of the signature signal of a certain transmitter, the possible bit or symbol value associated with that transmitter at that point in time, and the actual received signal which includes all the signals from all the interfering transmissions. The weight of each path is the sum of the branch metrics in a complete path. The goal of a tree searching algorithm is to try to find the complete path through a tree with the lowest metric. With the present invention the metrics of multiple complete paths are not calculated. Rather, the metrics of individual branches in a tree are calculated in the process of locating one complete path through the tree and thereby defines one unknown characteristic of each of the co-channel, interfering signals needed to decode the signals.
A MUD algorithm within the TurboMUD system determines discrete estimates of the transmitted channel symbols, with the estimates then provided to a bank of single-user decoders (one decoder for each user) to recover the input bit streams of all transmitted signals. Two general types of multi-user detectors within the TurboMUD system are possible, namely those that provide hard outputs, which are discrete values, and those that provide soft outputs, which indicate both the discrete estimate and the probability that the estimate is correct. In basic terms, turbodecoding refers to breaking a large processing process into smaller pieces and performing iterative processing on the smaller pieces until the larger processing is completed, and this basic principle was applied to the MUD.
However, single-user decoders operating on hard values, or discrete integers, have unacceptable error rates when there is a large amount of interference or noise in the received signal. The reason is that discrete integers do not provide adequate confidence values on which the single-user decoder can operate. These decoders operate better on so-called soft inputs in which confidence values can range from −1 to 1, such as for instance 0.75 as opposed to being either −1 or +1. To provide soft values that can then be utilized by a single-user decoder, the multi-user detector chosen for the TurboMUD can generate these soft values. The invention described below will work with soft output or a hard output MUDs, or a combination of the two.
In general, soft or hard output versions of the optimum maximum likelihood multi-user detector (Verdu, Multiuser Detection, Cambridge University Press, 1998) or an M algorithm (as described, for instance, in Schlegel, Trellis Coding, IEEE Press, 1997) with a moderate to high value of M causes the Turbo MUD to require too many computations to keep up with real time transmissions. Using a fast, but inferior, multiuser detection scheme such as a linear-based detector or those detailed in the text “Multiuser Detection” by Sergio Verdu causes poor quality output when there are many interferers or users.
Moreover, when dealing with hand-held communications units such as wireless handsets, the amount of processing within the device is limited, directly limiting the amount of computational complexity that is allowed. In order to provide real-time performance both at a cell site and the handset, it therefore becomes important to be able to reduce the amount of computational complexity and processing time so as to achieve real-time performance.
A great number of communications and data transfer systems operate at or near a full capacity. Conventional receiver performance is unsatisfactory in the presence of co-channel interference. Furthermore, many receivers require prior knowledge of signal parameters such as phase and amplitude of the channel to perform processing functions without co-channel interference. What is needed is a means to increase the number of available channels, by reassigning channels to be perhaps slightly interfering, thereby increasing the overall throughput without increasing bandwidth. Such a system should provide an efficient means of jointly estimating symbols, channel amplitude, and data rate transmitted in a super-saturated communications channel. And, any such invention should have the ability to estimate the symbols and data rate, blindly without prior knowledge of channel amplitudes and phase.